On the least-squares model averaging interval estimator

<p>In many applications of linear regression models, randomness due to model selection is commonly ignored in post-model selection inference. In order to account for the model selection uncertainty, least-squares frequentist model averaging has been proposed recently. We show that the confidence interval from model averaging is asymptotically equivalent to the confidence interval from the full model. The finite-sample confidence intervals based on approximations to the asymptotic distributions are also equivalent if the parameter of interest is a linear function of the regression coefficients. Furthermore, we demonstrate that this equivalence also holds for prediction intervals constructed in the same fashion.</p

Approximate Bayesianity of Frequentist Confidence Intervals for a Binomial Proportion

<p>The well-known Wilson and Agresti–Coull confidence intervals for a binomial proportion <i>p</i> are centered around a Bayesian estimator. Using this as a starting point, similarities between frequentist confidence intervals for proportions and Bayesian credible intervals based on low-informative priors are studied using asymptotic expansions. A Bayesian motivation for a large class of frequentist confidence intervals is provided. It is shown that the likelihood ratio interval for <i>p</i> approximates a Bayesian credible interval based on Kerman’s neutral noninformative conjugate prior up to <i>O</i>(<i>n</i>^{− 1}) in the confidence bounds. For the significance level α ≲ 0.317, the Bayesian interval based on the Jeffreys’ prior is then shown to be a compromise between the likelihood ratio and Wilson intervals. Supplementary materials for this article are available online.</p

Acoustics-Controlled Microdroplet and Microbubble Fusion and Its Application in the Synthesis of Hydrogel Microspheres

Droplet fusion technology is a key technology for many droplet-based biochemical medical applications. By integrating a symmetrical flow channel structure, we demonstrate an acoustics-controlled fusion method of microdroplets using surface acoustic waves. Different kinds of microdroplets can be staggered and ordered in the symmetrical flow channel, proving the good arrangement effect of the microfluidic chip. This method can realize not only the effective fusion of microbubbles but also the effective fusion of microdroplets of different sizes without any modification. Further, we investigate the influence of the input frequency and peak-to-peak value of the driving voltage on microdroplets fusion, giving the effective fusion parameter conditions of microdroplets. Finally, this method is successfully used in the preparation of hydrogel microspheres, offering a new platform for the synthesis of hydrogel microspheres

Acoustics-Controlled Microdroplet and Microbubble Fusion and Its Application in the Synthesis of Hydrogel Microspheres

Droplet fusion technology is a key technology for many droplet-based biochemical medical applications. By integrating a symmetrical flow channel structure, we demonstrate an acoustics-controlled fusion method of microdroplets using surface acoustic waves. Different kinds of microdroplets can be staggered and ordered in the symmetrical flow channel, proving the good arrangement effect of the microfluidic chip. This method can realize not only the effective fusion of microbubbles but also the effective fusion of microdroplets of different sizes without any modification. Further, we investigate the influence of the input frequency and peak-to-peak value of the driving voltage on microdroplets fusion, giving the effective fusion parameter conditions of microdroplets. Finally, this method is successfully used in the preparation of hydrogel microspheres, offering a new platform for the synthesis of hydrogel microspheres

Acoustics-Controlled Microdroplet and Microbubble Fusion and Its Application in the Synthesis of Hydrogel Microspheres

Droplet fusion technology is a key technology for many droplet-based biochemical medical applications. By integrating a symmetrical flow channel structure, we demonstrate an acoustics-controlled fusion method of microdroplets using surface acoustic waves. Different kinds of microdroplets can be staggered and ordered in the symmetrical flow channel, proving the good arrangement effect of the microfluidic chip. This method can realize not only the effective fusion of microbubbles but also the effective fusion of microdroplets of different sizes without any modification. Further, we investigate the influence of the input frequency and peak-to-peak value of the driving voltage on microdroplets fusion, giving the effective fusion parameter conditions of microdroplets. Finally, this method is successfully used in the preparation of hydrogel microspheres, offering a new platform for the synthesis of hydrogel microspheres

Acoustics-Controlled Microdroplet and Microbubble Fusion and Its Application in the Synthesis of Hydrogel Microspheres

Droplet fusion technology is a key technology for many droplet-based biochemical medical applications. By integrating a symmetrical flow channel structure, we demonstrate an acoustics-controlled fusion method of microdroplets using surface acoustic waves. Different kinds of microdroplets can be staggered and ordered in the symmetrical flow channel, proving the good arrangement effect of the microfluidic chip. This method can realize not only the effective fusion of microbubbles but also the effective fusion of microdroplets of different sizes without any modification. Further, we investigate the influence of the input frequency and peak-to-peak value of the driving voltage on microdroplets fusion, giving the effective fusion parameter conditions of microdroplets. Finally, this method is successfully used in the preparation of hydrogel microspheres, offering a new platform for the synthesis of hydrogel microspheres

Access Issues and Training Needs of Teachers in the Adult and Community Sector

Optimization of S3 parameters. (XLS 65 kb

Additional file 2: Table S2. of Decoding breast cancer tissue–stroma interactions using species-specific sequencing

p-values and FDR from SAMlike_test_on_rpkms.py. (XLSX 14232 kb

Additional file 8: Table S7. of Decoding breast cancer tissue–stroma interactions using species-specific sequencing

Alignment Statistics. (PDF 657 kb

Additional file 3: Figure S1. of Decoding breast cancer tissue–stroma interactions using species-specific sequencing

Pipeline of data processing. Figure S2. Comparison with other methods to separate human and mouse reads, both in terms of sensitivity (A) and specificity (B). Figure S3. Comparison of three species (rat, mouse, human) separation of rat (R1–R3) and mouse (M4, M5) RNA-seq samples, similar to Fig. 1g but with absolute number of reads on the y axis. Figure S4. Comparison of the number of genes up- and downregulated in (A) MDA-MB-231 cells co-cultured with 3T3-L1 cells expressing DLL4 or GFP, (B) MDA-MB-231 cells on immobilized Fc-DLL4 or Fc, and (C) 3T3-L1 cells, expressing DLL4 or GFP, co-cultured with MDA-MB-231 cells. Figure S5. Whole-genome gene expression QC: Depth Saturation. Figure S6. Whole-Genome Gene Expression QC: Density Plots after TMM Normalization. Figure S7. Additional principal component analyses (PCA). Figure S8. Scatterplot of genes in Estrogen-related signaling, differentially expressed in MCF7 cells in vitro and MCF7 cells in tumor. Figure S9. Scatterplots of Table S8 comparison groups. Figure S10. Hierarchical Clustering. Figure S11. FINAK_BREAST_CANCER_SDPP_SIGNATURE: Scatterplot of genes in the stroma-derived prognostic predictor of breast cancer disease outcome (Finak et al. 2008 [59]), differentially expressed in (A) Mammary Gland (MG) compared to MCF7 tumor stroma, (B) MG compared to MDA-MB-231 tumor stroma, and MCF7 tumor stroma compared to MDA-MB-231 tumor stroma. (PDF 11548 kb